Source: XKCD

Intro to R

This is a beginners R course for incoming graduate students in political science at UCLA. The purpose of this course is to starting thinking about functional programming and data analysis in a systematic way using both base R and Tidyverse packages (dplyr, ggplot2, etc) to clean, analyze, and visualize real data. By the end of the course, students should have an understanding of object oriented languages, work flow, reproducibility, data munging, summarizing data, and visualizing data. Each of the lessons will take about 2 hours to complete.

Before you show up to the first course, please download R and R-Studio (here is a tutorial if you get stuck) and complete this Try-R short course that will introduce you to the very basics of R. I assume you will have a very basic understanding of R by Day 1 below. If you have any issues with the install process, please reach out to me before the course starts!

I would like everyone to complete this pre-session worksheet before the course begins to make sure we are all on the same page with the basics. If you do the Try-R course above, this should be very simple to complete. Please complete it, knit to PDF, and email to me before the sessions begin.

Pre-Session Worksheet

1: Intro to R

2: Data Munging

3: Summarizing Data

4: Plotting

200D R Bootcamp

This is an R course that builds off of the previous Intro to R lessons and is particularly designed to prepare graduate students for UCLA’s 200D course on causal inference with Chad Hazlett. I recommend downloading the entire course at once, which is a zipped file of the lessons, worksheet, and data:

Download the whole course here

Or you can download individual files here:

1: Reading and Writing Data

2: R Markdown

3: Assessing Balance

4: Regression Basics

5: Loops, Functions, and Apply



Other (to come)

Predicted Probabilities

Merging Datasets

Text Data

Basic Mapping

R Resources

A lot of the R Course material was borrowed and adapted from Jenny Bryan’s excellent UBC Stat 545 course. She goes much further in depth on all topics than I do. I strongly recommend referring back to this site as needed.

There are a number of very useful cheatsheets produced by the folks over at R-Studio that I refer to often.

I have also recently come across Software Carpentry’s lessons, several of which address programming with R. They look pretty good. If you check them out let me know what you think.

Text Mining with R [Site]


Past Teaching Experience

(Spring 2015) American Politics with Professor Mark Smith at UW

(Fall 2014) American Politics with Professor Becca Thorpe at UW

(Winter 2014) American Politics with Professor Megan Francis at UW